Systems and methods for anonymous behavioral-based records identification

ABSTRACT

Methods, systems, and computer-readable media for identifying users, records, and/or devices using anonymous source data records associated with consumer activity are described. In general, the consumer activity may relate to consumer interactions with devices and/or content or data through devices. The source data records may be structured, such as associating the source data records with a sequential timeline. Behavioral markers may be generated by the matching system and associated with the source data records. Each behavioral marker may be analyzed across all of the source data records to determine the effectiveness of the behavioral marker to identify unique source data records. One or more of the behavioral markers may be used to generate a behavioral fingerprint. A behavioral fingerprint may be used to identify unique records, devices, and/or users, including identification based on different sets of source data records.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/643,921 filed on Mar. 10, 2015, which claims the benefit of U.S.Provisional Application No. 61/950,393 filed on Mar. 10, 2014, thecontents of which are incorporated by reference in their entirety as iffully set forth herein.

FIELD OF INVENTION

The described technology generally relates to identification of consumerdata and, more specifically, to identifying anonymous consumer databased on behavioral markers associated therewith.

BACKGROUND

As the collection and use of digital information about consumers hasgrown, so have concerns about consumer privacy and data security.Service providers collect information associated with consumers andconsumer interactions with their services for marketing purposes and/orto be sold to other entities for consumer marketing purposes. However,as service providers, such as Internet service operators (for instance,Facebook®) and media content providers (such as a cable networkoperator) have increased the amount and sophistication of their datacollection efforts, consumers have become more aware of how theirinformation is being used. In addition, they have become increasinglyconcerned that their information may be used and collected in ways thatthey are not aware of or that they do not expect. In general, companieshave relied on the integration of data collected based on primary keysrelating to consumers such as name, address, phone and email address.This personally identifiable information (PII) may be used to match datarecords from disparate sources. However, consumers and privacy advocateshave voiced concern about such practices and government agencies haveinstituted regulations limiting the collection and/or use of PII.

Accordingly, what is needed is a system that allows for the anonymouscollection of information relating to consumer activity that is usefulfor marketing and other related purposed, while also protecting theidentity and privacy of consumers.

SUMMARY

This disclosure is not limited to the particular systems, devices andmethods described, as these may vary. The terminology used in thedescription is for the purpose of describing the particular versions orembodiments only, and is not intended to limit the scope.

As used in this document, the singular forms “a,” “an,” and “the”include plural references unless the context clearly dictates otherwise.Unless defined otherwise, all technical and scientific terms used hereinhave the same meanings as commonly understood by one of ordinary skillin the art. Nothing in this disclosure is to be construed as anadmission that the embodiments described in this disclosure are notentitled to antedate such disclosure by virtue of prior invention. Asused in this document, the term “comprising” means “including, but notlimited to.”

In an embodiment, a system for behavioral-based user recordsidentification using anonymous user information may include a processorand a non-transitory, computer-readable storage medium in operablecommunication with the processor. The computer-readable storage mediummay contain one or more programming instructions that, when executed,cause the processor to receive a plurality of source data recordscomprising anonymous information associated with user interactions witha plurality of client logic devices, correlate the source data with asequential timeline over a timeline duration, determine a plurality ofbehavioral markers for each of the plurality of source data records,generate at least one behavioral fingerprint based on an aggregation ofthe plurality of behavioral markers, and associate the at least onebehavioral fingerprint with at least one of the plurality of clientlogic devices.

In an embodiment, a computer-implemented method for behavioral-baseduser records identification using anonymous user information mayinclude, by a processor receiving a plurality of source data recordscomprising anonymous information associated with user interactions witha plurality of client logic devices, correlating the source data with asequential timeline over a timeline duration, determining a plurality ofbehavioral markers for each of the plurality of source data records,generating at least one behavioral fingerprint based on an aggregationof the plurality of behavioral markers, and associating the at least onebehavioral fingerprint with at least one of the plurality of clientlogic devices.

In an embodiment, a computer-readable storage medium havingcomputer-readable program code configured for behavioral-based userrecords identification using anonymous user information may includecomputer-readable program code configured to receive a plurality ofsource data records comprising anonymous information associated withuser interactions with a plurality of client logic devices,computer-readable program code configured to correlate the source datawith a sequential timeline over a timeline duration, computer-readableprogram code configured to determine a plurality of behavioral markersfor each of the plurality of source data records, computer-readableprogram code configured to generate at least one behavioral fingerprintbased on an aggregation of the plurality of behavioral markers, andcomputer-readable program code configured to associate the at least onebehavioral fingerprint with at least one of the plurality of clientlogic devices.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects of the present invention will become morereadily apparent from the following detailed description taken inconnection with the accompanying drawings.

FIG. 1 depicts an illustrative record matching system according to someembodiments.

FIG. 2 depicts an illustrative record matching system according to someembodiments.

FIG. 3 depicts a flow diagram for an illustrative method of matchingrecords using behavioral markers according to some embodiments.

FIG. 4 illustrates various embodiments of a computing device forimplementing the various methods and processes described herein.

DETAILED DESCRIPTION

The described technology generally relates to systems, methods, andcomputer readable media for matching and/or identifying source datarecords of consumer activity. The source data records may be associatedwith user (or “consumer”) interactions with content presentationdevices, computing devices, consumer electronic devices, retailers,marketers, or other entities and/or devices capable of generating sourcedata records. In some embodiments, interactions may include, withoutlimitation, television viewing, downloading, Internet activity, socialnetwork activity, electronic program guide activity, personal videorecorder activity, purchasing activity, survey activity, or the like. Insome embodiments, at least a portion of the source data records mayinclude anonymous (or “anonymized” or “de-identified”) data that doesnot include or substantially does not include any personal identifiableinformation (PII) and/or cannot otherwise reasonably be used to identifyan individual, computing device, and/or records relating thereto. Insome embodiments, a behavioral-based record identification (or“matching”) system (the “system” or the “matching system”) may beconfigured to process, fingerprint, and/or match source data records.The matching system may be configured to receive source data records,including anonymous source data records, and to associate them with asequential timeline. Behavioral markers may be generated by the matchingsystem and associated with the source data records. Each behavioralmarker may be analyzed across all of the source data records todetermine the effectiveness of the behavioral marker to identify uniquesource data records. One or more of the behavioral markers may be usedto generate a behavioral fingerprint. In some embodiments, thebehavioral fingerprints generated on a first set of source data recordsmay be used to identify users (or “consumers”), source data records,devices, or the like associated with a second set of source datarecords. In some embodiments, the matching system does not use orsubstantially does not use any source data records or any informationcontaining PII or other consumer privacy identifiers.

The source data records may include any type of record capable of beinggenerated based on user interaction with a device and/or content througha medium. Illustrative mediums may include audio and visual mediums suchas television (or “TV”), radio, and broadcast, cable, satellite, and/ornetwork (e.g., Internet) forms thereof. Examples of content may include,but are not limited to, video, audio, movies, video games, televisionand radio programs, commercials, websites, images, photographs, text,electronic or digital documents, information feeds, streaming media,social media, social networks, and/or combinations thereof. The sourcedata records may include information relating to a device, content,and/or user interactions therewith. For example, a source data recordfor a set-top-box may include information relating to location, serviceprovider, program guides, channel selection, content, activity relatingto any of the foregoing (for example, selection of a program from aprogram guide), or the like.

A device generally refers to any device now known to those havingordinary skill in the art or developed in the future that is capable ofpresenting or providing access to content and/or data to a user.Non-limiting examples of devices may include televisions, smarttelevisions, laptops, personal digital assistants (PDAs), tabletcomputing devices, smartphones, personal computers (PCs), radios, audiodevices, electronic reading devices (“e-readers”), set-top-boxes,satellite receivers, video-on-demand (VOD) receivers, content receivers(e.g., Apple TV® manufactured by Apple Inc. of Cupertino, Calif., UnitedStates; Roku® manufactured by Roku, Inc. of Saratoga, Calif., UnitedStates), digital video recorders (DVRs), personal video recorders(PVRs), hard drives, flash drives, storage servers, digital video disc(DVD) devices, Blu-ray™ devices, in-car entertainment systems, or thelike.

Service providers and marketing companies (“marketers”) have long reliedon the collection of PII as part of their operating procedures,including for marketing purposes. The PII may be used to match datarecords from disparate sources. However, this activity has beencurtailed due to consumer privacy concerns and government regulation.Nonetheless, the growing use of Internet-connected devices continuallygenerates an enormous amount of consumer data that can and is being usedby service providers and marketers. The digital footprints generated byuse of computing devices by consumers has also generated new types ofidentifiers such as Internet Protocol (IP) addresses, cookies, devicefingerprints, user profiles, social login handles, or the like, whichcan and are being used by service providers and marketers to fill inpreviously unknown gaps in consumer behavior.

In general, digital footprints are not a digital identity; however, thecontent and metadata collected potentially impacts upon consumerprivacy, trust, security, and the reputation of companies that collectand make use of such information. For example, digital footprints may beused to infer personal information, such as demographic traits, gender,viewpoint affiliations, personality, or intelligence without anindividuals' knowledge.

The Federal Trade Commission (FTC) has played an active and prominentrole in offline and online privacy issues. Traditionally, the FTC hastaken the position that self-regulation by industry is the mosteffective way to manage the privacy of personal data. As such, marketersand other industry participants have developed, and generally advocate,that businesses follow best practices when it comes to the sharing ofpersonal data as well as records of online and/or offline consumerbehavior. One illustrative best practice requires that an organizationnot share PII with an outside party without notifying the individual towhom the information relates and, in some circumstances, seekingconsent. Overall, the focus has been on PII (such as name, address,phone, email address, etc.). Nevertheless, there is a growing need tobetter ensure that digital records of individuals' online and offlinebehaviors are also safely managed and only shared in an anonymizedbasis.

In some particular industries, such as the medical and financial fields,laws and regulations have been developed regulate the sharing ofconsumer information, particularly information including PII.Nonetheless, companies in non-regulated fields also realize that theymust proactively enact best practices that enable the use of collectedinformation for marketing and other purposes, while also looking toprotect consumer privacy, or risk a consumer reaction or regulation thatimposes rules that govern use of consumer data. In addition, companiesand marketers are increasingly challenged with managing the volume ofraw data and, in particular, with determining and locating the share ofthat volume that actually has value for the company or for marketers.With increasing volumes of complex, unstructured data, companies areincreasingly relying on digital linking to piece together trails ofconsumer behaviors related to, for example, online and offlinepurchases, media impressions and interactions, including mobile phoneusage, TV viewing data, and more.

From a data integration perspective, identifiers such as IP addresses,cookies, device fingerprints and social login handles presentsignificant challenges because they are anonymous and often verytransient. For example, IP addresses may be generated when any deviceinteracts on the Internet, such as a smartphone, Internet-enabled TV,game console, in-car entertainment system, or home appliance. Valuableinformation may be derived by matching data based on IP addresses, butsince Internet Services Providers (ISPs) often assign dynamic IPaddresses, different devices may appear to have the same address overtime. In addition, multiple devices may be assigned the same IP addresswhen they are on the same network. This can be useful in terms oflooking to gain insights across devices within a network, but presentssignificant challenges when IP addresses are assigned to corporatenetworks or IP blocks, such as multi-household dwelling units.

At present, cookies are a typical data exchange method used inconjunction with digital marketing activities. The InteractiveAdvertising Bureau (IAB) defines a cookie as a small text file sent by awebsite's server to be stored on the user's web-enabled device that isreturned unchanged by the user's device on subsequent interactions tothe server. The cookie may enable the website domain to associate datawith a specific device which distinguishes it from different devices.From a data integration perspective, cookies present challenges in thatthey can differ across browsers even on the same device. Cookies canalso be readily deleted by users. In addition, the breadth and use ofincreasingly persistent cookies is increasingly viewed as intrusive andnot consumer-friendly.

Because of the challenges associated with the use of cookies, somecompanies have seen an opportunity to create an alternative method fortracking devices. Such alternative solutions rely on extractinginformation about a device's configuration, in addition to the IPaddress, to create a unique device signature. Such an approach mayidentify unique devices 99% of the time. Nonetheless, marketers arereluctant to use such device identification techniques due to potentialconsumer privacy concerns since, unlike cookies, a user simply cannotdelete a device fingerprint to avoid being tracked.

Companies are interested in extracting greater value from theircollected data by enhancing their data with relevant third party data.However, conventional technology requires that companies trust thirdparties with personally identifiable information. Most companies arereluctant to share their consumer data records for the purpose ofappending relevant additional information. Certain companies rely on theuse of relatively weaker identifiers (for instance, IP addresses orcookies) to integrate and append relevant consumer data. However, from abusiness perspective, such approaches are typically less reliable andare still associated with consumer privacy concerns.

Companies and marketers often obtain data from third parties and/or selltheir own data to third parties. Companies that acquire the right to useand/or resell consumer data often face contractual limitations thatlimit their ability to share or sell consumer and/or device-level datarecords with other companies. For example, a company that has licensedthe use of television set-top-box (STB) data from various cable,satellite, or other TV distribution companies, may be interested inmatching its data with other companies that have licensed similar dataas such activity may provide a broader and/or a deeper perspective ofthe related data sets. However, companies interested in such matchingface considerable restrictions in their ability to exchange any PII, IPaddresses, device IDs, device related fingerprints, or the like as wellas any raw data records relating thereto. Consequently, the ability forthese companies to better understand or enhance their consumer datarelated offerings is currently restricted.

Although the growth in availability of data provides opportunities forcompanies to better understand consumer behavior, any form of datalinking must balance the reliability of the matching with consumerprivacy concerns. In addition, for companies that engage in the businessof developing and selling consumer data-driven products and services,the ability to extract value from the collection and use of data islimited by their source-data contractual agreements and present datamatching capabilities.

Accordingly, some embodiments provide systems and methods that enableindependent parties to separately generate a reliable matchingidentification using behavioral fingerprint markers and to do so withoutusing any or substantially without and PII, IP addresses, device IDs,device related fingerprints, or the like. In some embodiments, thebehavioral fingerprint marker may correspond with digital footprints ofanonymized consumer behavior. In some embodiments, the source data (orsource data records) may include complex structured or unstructuredsource data where there are needs for reliable matching of uniquerecords, including data that involves consumer privacy sensitivitiesand/or companies that engage in the business of developing and sellingconsumer data-driven products and services.

In some embodiments, the matching system may analyze digital footprintsof anonymized consumer behavior to determine suitable aggregated markersthat serve to distinguish records from one another. The methods andsystems described according to some embodiments may seem similar to DNAprofiling, which is a technique employed by forensic scientists toassist in the identification of individuals by their respective DNAprofiles. However, unlike the use of DNA profiles that reflect a portionof a person's DNA makeup, the methods and systems described according tosome embodiments involve the aggregation and/or classification ofpartial components of the underlying source data, such that theresulting markers are may not be part of the underlying source data. Insome embodiments, the source data records may include complex structuredand/or unstructured source data, including, but not limited to,exposures and/or interactions on the Internet, mobile phones, TV, gameconsoles, home appliances, or the like.

FIG. 1 depicts an illustrative matching according to some embodiments.As shown in FIG. 1, the matching system 100 may include one or moreserver logic devices 110, which may generally include a processor, anon-transitory memory or other storage device for housing programminginstructions, data or information regarding one or more applications,and other hardware, including, for example, the central processing unit(CPU) 405, read only memory (ROM) 410, random access memory (RAM) 415,communication ports 440, controller 420, and/or memory device 425depicted in FIG. 4 and described below in reference thereto.

In some embodiments, the programming instructions may include abehavioral-based record matching application (the “matching application”or the “application”) configured to, among other things, generatebehavioral fingerprints based on source data records and to use thebehavioral fingerprints to match other source data records. The serverlogic devices 110 may be in operable communication with client logicdevices 105, including, but not limited to, devices including, withoutlimitation, server computing devices, personal computers (PCs), kioskcomputing devices, mobile computing devices, laptop computers,smartphones, PDAs, global positioning system (GPS) devices, televisions(including, Internet-connected televisions or “smart” televisions),printing devices, tablet computing devices, in-car entertainment (ICE)systems, set-top-boxes, PVRs, DVRs, or any other logic and/or computingdevices now known or developed in the future.

The matching application may be configured to receive data sourcerecords from the client logic devices. In some embodiments, the datasource records may be received by the server logic devices 110 through aservice provider 120, other entity, communication device (for instance,a network edge device), or a software platform operated thereby. In someembodiments, the data source records and/or a portion thereof may beanonymous. In some embodiments, the matching application may onlyreceive anonymous data source records. In some embodiments, the datasource records from the client logic devices 105 may include PII and maybe de-identified by the service provider 120 and/or by the matchingapplication. The data source records may be stored within one or moredatabases 115.

In some embodiments, the matching application may be configured toassociate the source data records with a sequential timeline thatrepresents a time duration. For instance, the time duration may beconfigured based on time, such as a 24-hour day or day-parts, such asmorning, afternoon, evening, prime-time, workday, weekdays, weekend,hour-based segments (for example, 8-hour segments, 4-hour segments, orthe like). In another instance, TV viewing data derived from aset-top-box, a smart TV, a gaming console, or the like may have a timeduration based on a day of TV viewing. In some embodiments, the timeduration may be based on one or more events, such as a time durationrelating to a sporting event, concert, news event, media presentation,or the like.

In some embodiments, the matching application may be configured togenerate, create, or otherwise develop behavioral markers associatedwith the source data records. In some embodiments, the behavioralmarkers may be generated based on an aggregation and/or classificationof the source data records. In some embodiments, the behavioral markersmay be generated based on the same portion of each source data record,such as the same time period within the time duration. For example, TVviewing data may be aggregated and classified such that for eachindividual source data record there is a behavioral marker thatcorresponds to the TV network with the most associated time spentviewing during each quarter hour of each day (or nothing if theset-top-box is inactive, indicating that the TV is inactive). Forinstance, certain data source records (for example, associated with afirst device) may have Channel 1 at time period 1 as a behavioral markerand certain other data source records may have Channel 2 at time period2. In another example, certain data source records may have a behavioralmarker indicating “second screen” activity in which a user interactswith a second device, such as a mobile device, while watching a firstdevice, such as a TV.

In some embodiments, a plurality of different behavioral markers may begenerated and associated with the source data records. For example, forInternet-usage source data records, a first behavioral marker may begenerated relating to video content consumption and a second behavioralmarker may be generated relating to social network activity. In someembodiments, a data record timeline may be generated that includes atimeline with associated source data records that may be accessed,viewed, deleted, or otherwise modified by selecting a correspondingportion of the timeline.

The matching application may aggregate the behavioral markers over thetime duration, or a portion thereof, to generate a behavioralfingerprint. In some embodiments, the behavioral fingerprint may besufficiently unique to be associated with a client logic device 105, auser, or other entity or segment (such as a service provider,demographic segment, or the like). For instance, over the course of anyfull day of TV viewing behavior, the TV viewing behavioral fingerprintgenerated from the aggregated behavioral markers may be unique enough tobe associated with individual client logic devices 105.

Statistical analysis of the behavioral markers may be performed by thematching application according to some embodiments. In some embodiments,the statistical analysis may be performed across at least a portion,including all of or substantially all, of the behavioral markers acrossall or substantially all of the source data records to determine, amongother things, how effective each behavioral marker is at identifyingunique records. In some embodiments, the statistical analysis mayinclude identifying and/or assessing how sets of behavioral markers maybe combined to identify unique records. For instance, differentbehavioral marks and/or combinations thereof may be more effective forcertain types of interactions. For example, certain behavioral marksand/or combinations thereof may be more effective for TV viewing whileothers may be more effective for Internet activity. The behavioralmarkers and/or analysis information associated with the analysis thereofmay be stored within the one or more databases 115.

In some embodiments, the behavioral fingerprints generated based on afirst set of source data records may be applied to a second set ofrecords, such as an independent set of similar records. FIG. 2 depictsan illustrative record matching system configured to apply behavioralfingerprints to multiple sets of records according to some embodiments.As shown in FIG. 2, a first matching system 205 a may be configured toreceive source data records from a first set of devices 220 a-c througha first data source 201 a. The first matching system 205 a may beconfigured to generate behavioral markers and at least one behavioralfingerprint according to some embodiments for TV viewing source datarecords associated with TV devices 220 c. The behavioral markers andbehavioral fingerprints may be stored in a matching database 225 a.

The resulting behavioral markers and/or behavioral fingerprints fromindependently sourced data can be used to reliably match individualrecords, without using any PII or other consumer privacy sensitiveidentifiers. Accordingly, the behavioral markers and/or behavioralfingerprints generated by the first matching system 205 may be used tomatch similar data sets across companies, matching systems, or otherentities while complying with restrictions regarding any sharing ofsource data. For example, a second matching system 205 a may beconfigured to receive source data records from a second set of devices220 d-f through a second data source 201 b. In some embodiments, thesecond matching system 205 b may be configured to receive behavioralmarkers and/or behavioral fingerprints from the first matching system205 a (for instance, “external behavioral markers” or “externalbehavioral fingerprints”) and may store them, for example, in matchingdatabase 225 b. In some embodiments, the second matching system 205 bmay use the behavioral markers to generate one or more behavioralfingerprints according to its own processes, preferences, settings,parameters, or the like. In some embodiments, the second matching system205 b may use the behavioral fingerprints to match individual records,including its own records and/or records from other entities. Forexample, one company that collects TV viewing source data recordsderived from set-top-box data may match records with another companythat collects TV viewing source data records derived fromInternet-connected Smart TVs enabled with automated content recognition(ACR). According to some embodiments, matching the resulting behavioralfingerprints may allow the companies to know which records they have incommon.

Once a specific type of behavioral fingerprint has been established, forexample, by the first matching system 205 a, the behavioral fingerprintmay be used by companies to match source data records without having toexplicitly share the underlying source data, or any PII or otherconsumer privacy sensitive unique identifiers. For instance, the secondmatching system 205 b may send a request or otherwise communicate withthe first matching system 205 b requesting behavioral fingerprintsgenerated and analyzed by the first matching system, such as behavioralfingerprints relating to certain activity, such as TV viewing using amobile device. In response, the first matching system 205 a may transmitthe requested behavioral fingerprint to the second matching system 205b. In another embodiment, the first matching system 205 a may providethe second matching system 205 b with one or more behavioral markers forgenerating its own behavioral fingerprints. For example, the firstmatching system 205 a may provide one or more behavioral markersindicated as being effective for a particular type of deviceinteraction, such as Internet-based activity on a computing device.

In some embodiments, either entity could also then append aggregated orclassified information related to any of the behavioral fingerprintmatched records. For example, a company that collects TV viewing recordsderived from set-top-box data may have developed a sophisticatedconsumer segmentation scheme, which can then be appended to the matchingrecords for the company that collects TV viewing records derived fromInternet-connected, ACR-enabled Smart TVs. By matching the resultingbehavioral fingerprints, the companies could know which records theyhave in common.

FIG. 3 depicts a flow diagram for an illustrative methodbehavioral-based user records identification using anonymous userinformation according to some embodiments that may be performed by thematching system, such as through one or more server logic devices,arranged in accordance with at least some embodiments described herein.Example methods may include one or more operations, functions or actionsas illustrated by one or more of blocks 305, 310, 315, 320, 325, and/or330. The operations described in blocks 305-330 may also be stored ascomputer-executable instructions in a computer-readable medium such thememory elements 410, 415, and 425 depicted in FIG. 4. Althoughillustrated as discrete blocks, various blocks may be divided intoadditional blocks, combined into fewer blocks, or eliminated, dependingon the desired implementation. The operations described in blocks305-330 may be performed by a content developer, a content distributor,a content provider, a content presentation device, a network system, abroadcast network, or any combination thereof.

As shown in FIG. 3, the matching system may receive 305 source datarecords. For example, the matching system may have communicationpathways with devices and/or source data record providers, such asservice providers, marketers, or the like. The matching system maystructure 310 the source data records. For instance, the matchingapplication may position, correlate, arrange, or otherwise associate thesource data records with a sequential timeline. The matching applicationmay generate 315 behavioral markers for the source data records. In someembodiments, the behavioral markers may be developed using aggregationand/or classification of a same portion of each source data record.

The effectiveness of the behavioral markers may be analyzed 320 todetermine, for example, how well each marker is able to identify uniquerecords and/or the effectiveness of combinations of the behavioralmarkers. In some embodiments, the analysis of the behavioral markers mayinclude a statistical analysis. The matching application may generate325 one or more behavioral fingerprints based on at least one of thebehavioral markers. In some embodiments, the behavioral fingerprints mayinclude various combinations of behavioral markers and/or modificationsto the behavioral markers. The behavioral fingerprints may be used tomatch 330 record, such as source data records. In some embodiments, theentity and/or system generating the behavioral fingerprints may bedifferent from the entity and/or system matching the records.

FIG. 4 depicts a block diagram of exemplary internal hardware that maybe used to contain or implement the various computer processes andsystems as discussed above. A bus 400 serves as the main informationhighway interconnecting the other illustrated components of thehardware. CPU 405 is the central processing unit of the system,performing calculations and logic operations required to execute aprogram. CPU 405 is an exemplary processing device, computing device orprocessor as such terms are used within this disclosure. Read onlymemory (ROM) 430 and random access memory (RAM) 435 constitute exemplarymemory devices.

A controller 420 interfaces with one or more optional memory devices 425via the system bus 400. These memory devices 425 may include, forexample, an external or internal DVD drive, a CD ROM drive, a harddrive, flash memory, a USB drive or the like. As indicated previously,these various drives and controllers are optional devices. Additionally,the memory devices 425 may be configured to include individual files forstoring any software modules or instructions, auxiliary data, commonfiles for storing groups of results or auxiliary, or one or moredatabases for storing the result information, auxiliary data, andrelated information as discussed above.

Program instructions, software or interactive modules for performing anyof the functional steps associated with the determination,configuration, transmission, decoding, or the like of the presentationsettings as described above may be stored in the ROM 430 and/or the RAM435. Optionally, the program instructions may be stored on a tangiblecomputer-readable medium such as a compact disk, a digital disk, flashmemory, a memory card, a USB drive, an optical disc storage medium, suchas a Blu-ray™ disc, and/or other recording medium.

An optional display interface 430 can permit information from the bus400 to be displayed on the display 435 in audio, visual, graphic oralphanumeric format. Communication with external devices may occur usingvarious communication ports 440. An exemplary communication port 440 maybe attached to a communications network, such as the Internet or a localarea network.

The hardware may also include an interface 445 which allows for receiptof data from input devices such as a keyboard 450 or other input device455 such as a mouse, a joystick, a touch screen, a remote control, apointing device, a video input device and/or an audio input device.

It will be appreciated that various of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or applications. It will alsobe appreciated that various presently unforeseen or unanticipatedalternatives, modifications, variations or improvements therein may besubsequently made by those skilled in the art which alternatives,variations and improvements are also intended to be encompassed by thefollowing claims.

What is claimed is:
 1. A method comprising: receiving, from one or morecomputing devices, first data comprising anonymous informationassociated with user interactions with the one or more computingdevices; determining, based on the first data, second data indicatingbehavioral information associated with the user interactions;generating, based on the behavioral information, one or more behavioralmarkers; generating, based on the one or more behavioral markers, one ormore fingerprints associated with the one or more computing devices; anddetermining, based on the one or more fingerprints, identifyinginformation of one or more users of the one or more computing devices.2. The method of claim 1, wherein the determining the second datacomprises: determining, based on the first data, a timeline of the userinteractions over a time duration.
 3. The method of claim 2, wherein atleast a portion of the first data comprises unstructured data and thedetermining the second data further comprises: matching at least aportion of the unstructured data with a time on the timeline.
 4. Themethod of claim 2, wherein the generating the one or more behavioralmarkers comprises: aggregating at least a portion of the behavioralinformation over the time duration.
 5. The method of claim 1, whereinthe one or more fingerprints indicate information unique to the one ormore computing devices.
 6. The method of claim 1, wherein the userinteractions include at least one of television viewing, downloading afile, Internet activity, social network activity, electronic programguide activity, personal video recorder activity, or purchasingactivity.
 7. The method of claim 1, wherein the determining theidentifying information indicates an effectiveness of the one or morebehavioral markers or the one or more fingerprints.
 8. A devicecomprising: one or more processors; and memory storing instructionsthat, when executed by the one or more processors, cause the device to:receive, from one or more computing devices, first data comprisinganonymous information associated with user interactions with the one ormore computing devices; determine, based on the first data, second dataindicating behavioral information associated with the user interactions;generate, based on the behavioral information, one or more behavioralmarkers; and generate, based on the one or more behavioral markers, oneor more fingerprints associated with the one or more computing devices;and determine, based on the one or more fingerprints, identifyinginformation of one or more users of the one or more computing devices.9. The device of claim 8, wherein the determining the second datacomprises: determining, based on the first data, a timeline of the userinteractions over a time duration.
 10. The device of claim 9, wherein atleast a portion of the first data comprises unstructured data and thedetermining the second data further comprises: matching at least aportion of the unstructured data with a time on the timeline.
 11. Thedevice of claim 9, wherein the generating the one or more behavioralmarkers comprises: aggregating at least a portion of the behavioralinformation over the time duration.
 12. The device of claim 8, whereinthe one or more fingerprints indicate information unique to the one ormore computing devices.
 13. The device of claim 8, wherein the userinteractions include at least one of television viewing, downloading afile, Internet activity, social network activity, electronic programguide activity, personal video recorder activity, or purchasingactivity.
 14. The device of claim 8, wherein the determining theidentifying information indicates an effectiveness of the one or morebehavioral markers or the one or more fingerprints.
 15. A non-transitorycomputer-readable storage medium storing computer-readable instructionsthat, when executed by a processor, cause: receiving, from one or morecomputing devices, first data comprising anonymous informationassociated with user interactions with the one or more computingdevices; determining, based on the first data, second data indicatingbehavioral information associated with the user interactions;generating, based on the behavioral information, one or more behavioralmarkers; generating, based on the one or more behavioral markers, one ormore fingerprints associated with the one or more computing devices; anddetermining, based on the one or more fingerprints, identifyinginformation of one or more users of the one or more computing devices.16. The non-transitory computer-readable storage medium of claim 15,wherein the determining the second data comprises: determining, based onthe first data, a timeline of the user interactions over a timeduration.
 17. The non-transitory computer-readable storage medium ofclaim 16, wherein at least a portion of the first data comprisesunstructured data and the determining the second data further comprises:matching at least a portion of the unstructured data with a time on thetimeline.
 18. The non-transitory computer-readable storage medium ofclaim 16, wherein the generating the one or more behavioral markerscomprises: aggregating at least a portion of the behavioral informationover the time duration.
 19. The non-transitory computer-readable storagemedium of claim 15, wherein the one or more fingerprints indicateinformation unique to the one or more computing devices.
 20. Thenon-transitory computer-readable storage medium of claim 15, wherein theuser interactions include at least one of television viewing,downloading a file, Internet activity, social network activity,electronic program guide activity, personal video recorder activity, orpurchasing activity.